Malicious Anomaly Detection and Prediction Using Prophet Luminol and RNN

Machine learning (ML) has brought about many advancements in today's world which is benefiting humans and machines. Because of the growth of digital services, malicious anomalies have increased on the transmission media. This has compromised crucial and important data of several organizations and companies. Vulnerabilities in network have increased in recent years and problems such as loss of data have increased. The process of detecting anomalies in network traffic becomes very important activity. An effective system for detection of these anomalies should be utilized to stop such attacks. Machine learning mechanisms for anomaly detection can prove to be the most accurate and best form of detecting malware or botnets on network so that we can prevent further threats. In this work one can understand the new implementation methods for anomaly detection. By importing python libraries and building ML models, one can learn certain outliers and different anomalies presence.